Differential prefrontal-like deficit in children after cerebellar astrocytoma and medulloblastoma tumor

BACKGROUND This study was realized thanks to the collaboration of children and adolescents who had been resected from cerebellar tumors. The medulloblastoma group (CE+, n = 7) in addition to surgery received radiation and chemotherapy. The astrocytoma group (CE, n = 13) did not receive additional treatments. Each clinical group was compared in their executive functioning with a paired control group (n = 12). The performances of the clinical groups with respect to controls were compared considering the tumor's localization (vermis or hemisphere) and the affectation (or not) of the dentate nucleus. Executive variables were correlated with the age at surgery, the time between surgery-evaluation and the resected volume. METHODS The executive functioning was assessed by means of WCST, Complex Rey Figure, Controlled Oral Word Association Test (letter and animal categories), Digits span (WISC-R verbal scale) and Stroop test. These tests are very sensitive to dorsolateral PFC and/or to medial frontal cortex functions. The scores for the non-verbal Raven IQ were also obtained. Direct scores were corrected by age and transformed in standard scores using normative data. The neuropsychological evaluation was made at 3.25 (SD = 2.74) years from surgery in CE group and at 6.47 (SD = 2.77) in CE+ group. RESULTS The Medulloblastoma group showed severe executive deficit (

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).